Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations4600
Missing cells22800
Missing cells (%)17.1%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.0 MiB
Average record size in memory232.0 B

Variable types

Text21
DateTime1
Numeric5
Unsupported1
Categorical1

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
Amazon Playlist Count is highly overall correlated with Apple Music Playlist Count and 1 other fieldsHigh correlation
Apple Music Playlist Count is highly overall correlated with Amazon Playlist Count and 1 other fieldsHigh correlation
Deezer Playlist Count is highly overall correlated with Amazon Playlist Count and 1 other fieldsHigh correlation
Spotify Streams has 113 (2.5%) missing values Missing
Spotify Playlist Count has 70 (1.5%) missing values Missing
Spotify Playlist Reach has 72 (1.6%) missing values Missing
Spotify Popularity has 804 (17.5%) missing values Missing
YouTube Views has 308 (6.7%) missing values Missing
YouTube Likes has 315 (6.8%) missing values Missing
TikTok Posts has 1173 (25.5%) missing values Missing
TikTok Likes has 980 (21.3%) missing values Missing
TikTok Views has 981 (21.3%) missing values Missing
YouTube Playlist Reach has 1009 (21.9%) missing values Missing
Apple Music Playlist Count has 561 (12.2%) missing values Missing
AirPlay Spins has 498 (10.8%) missing values Missing
SiriusXM Spins has 2123 (46.2%) missing values Missing
Deezer Playlist Count has 921 (20.0%) missing values Missing
Deezer Playlist Reach has 928 (20.2%) missing values Missing
Amazon Playlist Count has 1055 (22.9%) missing values Missing
Pandora Streams has 1106 (24.0%) missing values Missing
Pandora Track Stations has 1268 (27.6%) missing values Missing
Soundcloud Streams has 3333 (72.5%) missing values Missing
Shazam Counts has 577 (12.5%) missing values Missing
TIDAL Popularity has 4600 (100.0%) missing values Missing
TIDAL Popularity is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-01-30 07:13:16.352590
Analysis finished2025-01-30 07:13:23.671878
Duration7.32 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Track
Text

Distinct4370
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:24.357055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length114
Median length74
Mean length16.598696
Min length1

Characters and Unicode

Total characters76354
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4188 ?
Unique (%)91.0%

Sample

1st rowMILLION DOLLAR BABY
2nd rowNot Like Us
3rd rowi like the way you kiss me
4th rowFlowers
5th rowHoudini
ValueCountFrequency (%)
feat 469
 
3.3%
421
 
3.0%
the 276
 
2.0%
you 189
 
1.3%
me 185
 
1.3%
with 139
 
1.0%
love 131
 
0.9%
i 123
 
0.9%
from 110
 
0.8%
in 103
 
0.7%
Other values (5071) 11900
84.7%
2025-01-30T16:13:24.998715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9471
 
12.4%
e 5798
 
7.6%
a 5114
 
6.7%
o 3893
 
5.1%
i 3418
 
4.5%
n 2936
 
3.8%
t 2921
 
3.8%
r 2814
 
3.7%
l 2086
 
2.7%
s 1946
 
2.5%
Other values (79) 35957
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9471
 
12.4%
e 5798
 
7.6%
a 5114
 
6.7%
o 3893
 
5.1%
i 3418
 
4.5%
n 2936
 
3.8%
t 2921
 
3.8%
r 2814
 
3.7%
l 2086
 
2.7%
s 1946
 
2.5%
Other values (79) 35957
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9471
 
12.4%
e 5798
 
7.6%
a 5114
 
6.7%
o 3893
 
5.1%
i 3418
 
4.5%
n 2936
 
3.8%
t 2921
 
3.8%
r 2814
 
3.7%
l 2086
 
2.7%
s 1946
 
2.5%
Other values (79) 35957
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9471
 
12.4%
e 5798
 
7.6%
a 5114
 
6.7%
o 3893
 
5.1%
i 3418
 
4.5%
n 2936
 
3.8%
t 2921
 
3.8%
r 2814
 
3.7%
l 2086
 
2.7%
s 1946
 
2.5%
Other values (79) 35957
47.1%
Distinct4005
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:25.474295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length123
Median length78
Mean length18.395435
Min length1

Characters and Unicode

Total characters84619
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3708 ?
Unique (%)80.6%

Sample

1st rowMillion Dollar Baby - Single
2nd rowNot Like Us
3rd rowI like the way you kiss me
4th rowFlowers - Single
5th rowHoudini
ValueCountFrequency (%)
680
 
4.5%
the 428
 
2.8%
single 368
 
2.4%
feat 281
 
1.8%
you 165
 
1.1%
me 147
 
1.0%
a 128
 
0.8%
from 122
 
0.8%
love 109
 
0.7%
i 97
 
0.6%
Other values (4998) 12692
83.4%
2025-01-30T16:13:26.042961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10642
 
12.6%
e 6227
 
7.4%
a 4796
 
5.7%
i 4012
 
4.7%
o 3962
 
4.7%
n 3417
 
4.0%
r 2963
 
3.5%
t 2817
 
3.3%
l 2619
 
3.1%
s 2264
 
2.7%
Other values (81) 40900
48.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10642
 
12.6%
e 6227
 
7.4%
a 4796
 
5.7%
i 4012
 
4.7%
o 3962
 
4.7%
n 3417
 
4.0%
r 2963
 
3.5%
t 2817
 
3.3%
l 2619
 
3.1%
s 2264
 
2.7%
Other values (81) 40900
48.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10642
 
12.6%
e 6227
 
7.4%
a 4796
 
5.7%
i 4012
 
4.7%
o 3962
 
4.7%
n 3417
 
4.0%
r 2963
 
3.5%
t 2817
 
3.3%
l 2619
 
3.1%
s 2264
 
2.7%
Other values (81) 40900
48.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10642
 
12.6%
e 6227
 
7.4%
a 4796
 
5.7%
i 4012
 
4.7%
o 3962
 
4.7%
n 3417
 
4.0%
r 2963
 
3.5%
t 2817
 
3.3%
l 2619
 
3.1%
s 2264
 
2.7%
Other values (81) 40900
48.3%

Artist
Text

Distinct1999
Distinct (%)43.5%
Missing5
Missing (%)0.1%
Memory size36.1 KiB
2025-01-30T16:13:26.487543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length33
Mean length9.8352557
Min length1

Characters and Unicode

Total characters45193
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1339 ?
Unique (%)29.1%

Sample

1st rowTommy Richman
2nd rowKendrick Lamar
3rd rowArtemas
4th rowMiley Cyrus
5th rowEminem
ValueCountFrequency (%)
the 113
 
1.4%
lil 86
 
1.1%
drake 63
 
0.8%
taylor 63
 
0.8%
swift 63
 
0.8%
bad 63
 
0.8%
bunny 60
 
0.7%
g 54
 
0.7%
dj 43
 
0.5%
42
 
0.5%
Other values (2804) 7394
91.9%
2025-01-30T16:13:26.984428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4143
 
9.2%
3458
 
7.7%
e 3273
 
7.2%
i 2688
 
5.9%
n 2414
 
5.3%
o 2252
 
5.0%
r 2244
 
5.0%
l 1884
 
4.2%
s 1299
 
2.9%
t 1126
 
2.5%
Other values (74) 20412
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45193
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4143
 
9.2%
3458
 
7.7%
e 3273
 
7.2%
i 2688
 
5.9%
n 2414
 
5.3%
o 2252
 
5.0%
r 2244
 
5.0%
l 1884
 
4.2%
s 1299
 
2.9%
t 1126
 
2.5%
Other values (74) 20412
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45193
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4143
 
9.2%
3458
 
7.7%
e 3273
 
7.2%
i 2688
 
5.9%
n 2414
 
5.3%
o 2252
 
5.0%
r 2244
 
5.0%
l 1884
 
4.2%
s 1299
 
2.9%
t 1126
 
2.5%
Other values (74) 20412
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45193
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4143
 
9.2%
3458
 
7.7%
e 3273
 
7.2%
i 2688
 
5.9%
n 2414
 
5.3%
o 2252
 
5.0%
r 2244
 
5.0%
l 1884
 
4.2%
s 1299
 
2.9%
t 1126
 
2.5%
Other values (74) 20412
45.2%
Distinct1562
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
Minimum1987-07-21 00:00:00
Maximum2024-06-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-30T16:13:27.124812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:27.467060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ISRC
Text

Distinct4598
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:27.754492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters55200
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4596 ?
Unique (%)99.9%

Sample

1st rowQM24S2402528
2nd rowUSUG12400910
3rd rowQZJ842400387
4th rowUSSM12209777
5th rowUSUG12403398
ValueCountFrequency (%)
uswl11700269 2
 
< 0.1%
tcagj2289254 2
 
< 0.1%
usum72404990 1
 
< 0.1%
uswb12402486 1
 
< 0.1%
usum72401991 1
 
< 0.1%
ussm12402041 1
 
< 0.1%
nlc242100307 1
 
< 0.1%
uswl12300002 1
 
< 0.1%
usug12401028 1
 
< 0.1%
usrc12301932 1
 
< 0.1%
Other values (4588) 4588
99.7%
2025-01-30T16:13:28.193616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7634
13.8%
2 6686
12.1%
1 5748
 
10.4%
U 3488
 
6.3%
3 3195
 
5.8%
S 2961
 
5.4%
4 2787
 
5.0%
7 2626
 
4.8%
9 2160
 
3.9%
8 2050
 
3.7%
Other values (26) 15865
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7634
13.8%
2 6686
12.1%
1 5748
 
10.4%
U 3488
 
6.3%
3 3195
 
5.8%
S 2961
 
5.4%
4 2787
 
5.0%
7 2626
 
4.8%
9 2160
 
3.9%
8 2050
 
3.7%
Other values (26) 15865
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7634
13.8%
2 6686
12.1%
1 5748
 
10.4%
U 3488
 
6.3%
3 3195
 
5.8%
S 2961
 
5.4%
4 2787
 
5.0%
7 2626
 
4.8%
9 2160
 
3.9%
8 2050
 
3.7%
Other values (26) 15865
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7634
13.8%
2 6686
12.1%
1 5748
 
10.4%
U 3488
 
6.3%
3 3195
 
5.8%
S 2961
 
5.4%
4 2787
 
5.0%
7 2626
 
4.8%
9 2160
 
3.9%
8 2050
 
3.7%
Other values (26) 15865
28.7%
Distinct4577
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:28.662535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.54
Min length1

Characters and Unicode

Total characters20884
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4554 ?
Unique (%)99.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5
ValueCountFrequency (%)
4,357 2
 
< 0.1%
1,103 2
 
< 0.1%
1,807 2
 
< 0.1%
2,192 2
 
< 0.1%
626 2
 
< 0.1%
1,156 2
 
< 0.1%
482 2
 
< 0.1%
1,724 2
 
< 0.1%
4,420 2
 
< 0.1%
559 2
 
< 0.1%
Other values (4567) 4580
99.6%
2025-01-30T16:13:29.416214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 3596
17.2%
3 2431
11.6%
1 2427
11.6%
2 2427
11.6%
4 2011
9.6%
5 1408
 
6.7%
7 1320
 
6.3%
9 1320
 
6.3%
6 1317
 
6.3%
8 1314
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 3596
17.2%
3 2431
11.6%
1 2427
11.6%
2 2427
11.6%
4 2011
9.6%
5 1408
 
6.7%
7 1320
 
6.3%
9 1320
 
6.3%
6 1317
 
6.3%
8 1314
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 3596
17.2%
3 2431
11.6%
1 2427
11.6%
2 2427
11.6%
4 2011
9.6%
5 1408
 
6.7%
7 1320
 
6.3%
9 1320
 
6.3%
6 1317
 
6.3%
8 1314
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 3596
17.2%
3 2431
11.6%
1 2427
11.6%
2 2427
11.6%
4 2011
9.6%
5 1408
 
6.7%
7 1320
 
6.3%
9 1320
 
6.3%
6 1317
 
6.3%
8 1314
 
6.3%

Track Score
Real number (ℝ)

Distinct862
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.844043
Minimum19.4
Maximum725.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:29.694565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.4
5-th percentile20.1
Q123.3
median29.9
Q344.425
95-th percentile101.8
Maximum725.4
Range706
Interquartile range (IQR)21.125

Descriptive statistics

Standard deviation38.543766
Coefficient of variation (CV)0.92112909
Kurtosis53.934539
Mean41.844043
Median Absolute Deviation (MAD)7.9
Skewness5.6882235
Sum192482.6
Variance1485.6219
MonotonicityDecreasing
2025-01-30T16:13:29.872929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.7 42
 
0.9%
19.6 40
 
0.9%
19.7 40
 
0.9%
20.5 37
 
0.8%
21.5 37
 
0.8%
21 37
 
0.8%
21.6 37
 
0.8%
20.3 33
 
0.7%
22.2 33
 
0.7%
20.1 33
 
0.7%
Other values (852) 4231
92.0%
ValueCountFrequency (%)
19.4 8
 
0.2%
19.5 33
0.7%
19.6 40
0.9%
19.7 40
0.9%
19.8 30
0.7%
19.9 27
0.6%
20 22
0.5%
20.1 33
0.7%
20.2 32
0.7%
20.3 33
0.7%
ValueCountFrequency (%)
725.4 1
< 0.1%
545.9 1
< 0.1%
538.4 1
< 0.1%
444.9 1
< 0.1%
423.3 1
< 0.1%
410.1 1
< 0.1%
407.2 1
< 0.1%
375.8 1
< 0.1%
355.7 1
< 0.1%
330.6 1
< 0.1%

Spotify Streams
Text

Missing 

Distinct4425
Distinct (%)98.6%
Missing113
Missing (%)2.5%
Memory size36.1 KiB
2025-01-30T16:13:30.286526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.764876
Min length5

Characters and Unicode

Total characters48302
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4366 ?
Unique (%)97.3%

Sample

1st row390,470,936
2nd row323,703,884
3rd row601,309,283
4th row2,031,280,633
5th row107,034,922
ValueCountFrequency (%)
1,655,575,417 4
 
0.1%
1,642,258,500 3
 
0.1%
777,305,444 2
 
< 0.1%
1,614,203,949 2
 
< 0.1%
2,031,280,633 2
 
< 0.1%
1,356,969,354 2
 
< 0.1%
1,431,126,152 2
 
< 0.1%
1,666,699,743 2
 
< 0.1%
579,189,526 2
 
< 0.1%
1,873,746,537 2
 
< 0.1%
Other values (4415) 4464
99.5%
2025-01-30T16:13:30.775765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 9398
19.5%
1 4860
10.1%
2 4249
8.8%
3 3958
8.2%
5 3907
8.1%
4 3817
7.9%
6 3724
 
7.7%
9 3668
 
7.6%
8 3643
 
7.5%
7 3563
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 9398
19.5%
1 4860
10.1%
2 4249
8.8%
3 3958
8.2%
5 3907
8.1%
4 3817
7.9%
6 3724
 
7.7%
9 3668
 
7.6%
8 3643
 
7.5%
7 3563
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 9398
19.5%
1 4860
10.1%
2 4249
8.8%
3 3958
8.2%
5 3907
8.1%
4 3817
7.9%
6 3724
 
7.7%
9 3668
 
7.6%
8 3643
 
7.5%
7 3563
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 9398
19.5%
1 4860
10.1%
2 4249
8.8%
3 3958
8.2%
5 3907
8.1%
4 3817
7.9%
6 3724
 
7.7%
9 3668
 
7.6%
8 3643
 
7.5%
7 3563
 
7.4%
Distinct4207
Distinct (%)92.9%
Missing70
Missing (%)1.5%
Memory size36.1 KiB
2025-01-30T16:13:31.177973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.589404
Min length1

Characters and Unicode

Total characters25320
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4055 ?
Unique (%)89.5%

Sample

1st row30,716
2nd row28,113
3rd row54,331
4th row269,802
5th row7,223
ValueCountFrequency (%)
1 46
 
1.0%
2 24
 
0.5%
3 19
 
0.4%
5 18
 
0.4%
4 14
 
0.3%
6 9
 
0.2%
8 8
 
0.2%
7 7
 
0.2%
12 7
 
0.2%
13 6
 
0.1%
Other values (4197) 4372
96.5%
2025-01-30T16:13:31.868112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 4010
15.8%
1 3193
12.6%
2 2488
9.8%
3 2259
8.9%
4 2036
8.0%
5 2006
7.9%
6 1985
7.8%
8 1914
7.6%
7 1851
7.3%
0 1802
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 4010
15.8%
1 3193
12.6%
2 2488
9.8%
3 2259
8.9%
4 2036
8.0%
5 2006
7.9%
6 1985
7.8%
8 1914
7.6%
7 1851
7.3%
0 1802
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 4010
15.8%
1 3193
12.6%
2 2488
9.8%
3 2259
8.9%
4 2036
8.0%
5 2006
7.9%
6 1985
7.8%
8 1914
7.6%
7 1851
7.3%
0 1802
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 4010
15.8%
1 3193
12.6%
2 2488
9.8%
3 2259
8.9%
4 2036
8.0%
5 2006
7.9%
6 1985
7.8%
8 1914
7.6%
7 1851
7.3%
0 1802
7.1%
Distinct4478
Distinct (%)98.9%
Missing72
Missing (%)1.6%
Memory size36.1 KiB
2025-01-30T16:13:32.350335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.1415636
Min length1

Characters and Unicode

Total characters41393
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4454 ?
Unique (%)98.4%

Sample

1st row196,631,588
2nd row174,597,137
3rd row211,607,669
4th row136,569,078
5th row151,469,874
ValueCountFrequency (%)
3 8
 
0.2%
1 7
 
0.2%
2 6
 
0.1%
4 6
 
0.1%
2,190 4
 
0.1%
20 3
 
0.1%
31 3
 
0.1%
10 3
 
0.1%
5 3
 
0.1%
17 3
 
0.1%
Other values (4468) 4482
99.0%
2025-01-30T16:13:33.000802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 8340
20.1%
1 4275
10.3%
2 3629
8.8%
3 3396
8.2%
6 3215
 
7.8%
4 3207
 
7.7%
5 3182
 
7.7%
7 3180
 
7.7%
8 3067
 
7.4%
0 2990
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 8340
20.1%
1 4275
10.3%
2 3629
8.8%
3 3396
8.2%
6 3215
 
7.8%
4 3207
 
7.7%
5 3182
 
7.7%
7 3180
 
7.7%
8 3067
 
7.4%
0 2990
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 8340
20.1%
1 4275
10.3%
2 3629
8.8%
3 3396
8.2%
6 3215
 
7.8%
4 3207
 
7.7%
5 3182
 
7.7%
7 3180
 
7.7%
8 3067
 
7.4%
0 2990
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 8340
20.1%
1 4275
10.3%
2 3629
8.8%
3 3396
8.2%
6 3215
 
7.8%
4 3207
 
7.7%
5 3182
 
7.7%
7 3180
 
7.7%
8 3067
 
7.4%
0 2990
 
7.2%

Spotify Popularity
Real number (ℝ)

Missing 

Distinct94
Distinct (%)2.5%
Missing804
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean63.501581
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:33.151103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q161
median67
Q373
95-th percentile80
Maximum96
Range95
Interquartile range (IQR)12

Descriptive statistics

Standard deviation16.186438
Coefficient of variation (CV)0.2548982
Kurtosis4.6711172
Mean63.501581
Median Absolute Deviation (MAD)6
Skewness-2.0519714
Sum241052
Variance262.00079
MonotonicityNot monotonic
2025-01-30T16:13:33.354490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 185
 
4.0%
70 176
 
3.8%
65 175
 
3.8%
66 170
 
3.7%
68 170
 
3.7%
71 166
 
3.6%
69 156
 
3.4%
64 151
 
3.3%
72 147
 
3.2%
62 144
 
3.1%
Other values (84) 2156
46.9%
(Missing) 804
 
17.5%
ValueCountFrequency (%)
1 26
0.6%
2 14
0.3%
3 12
0.3%
4 12
0.3%
5 6
 
0.1%
6 13
0.3%
7 9
 
0.2%
8 7
 
0.2%
9 5
 
0.1%
10 9
 
0.2%
ValueCountFrequency (%)
96 1
 
< 0.1%
95 1
 
< 0.1%
92 6
0.1%
91 1
 
< 0.1%
90 3
 
0.1%
89 1
 
< 0.1%
88 3
 
0.1%
87 14
0.3%
86 13
0.3%
85 11
0.2%

YouTube Views
Text

Missing 

Distinct4290
Distinct (%)> 99.9%
Missing308
Missing (%)6.7%
Memory size36.1 KiB
2025-01-30T16:13:33.873984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length13
Mean length10.67754
Min length3

Characters and Unicode

Total characters45828
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4288 ?
Unique (%)99.9%

Sample

1st row84,274,754
2nd row116,347,040
3rd row122,599,116
4th row1,096,100,899
5th row77,373,957
ValueCountFrequency (%)
30,913,276 2
 
< 0.1%
828,853,696 2
 
< 0.1%
20,942,928 1
 
< 0.1%
1,096,100,899 1
 
< 0.1%
77,373,957 1
 
< 0.1%
131,148,091 1
 
< 0.1%
308,723,145 1
 
< 0.1%
228,382,568 1
 
< 0.1%
32,735,244 1
 
< 0.1%
116,347,040 1
 
< 0.1%
Other values (4280) 4280
99.7%
2025-01-30T16:13:34.553172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 8987
19.6%
1 4686
10.2%
2 3952
8.6%
3 3861
8.4%
5 3642
7.9%
4 3551
 
7.7%
6 3482
 
7.6%
9 3463
 
7.6%
8 3458
 
7.5%
7 3398
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 8987
19.6%
1 4686
10.2%
2 3952
8.6%
3 3861
8.4%
5 3642
7.9%
4 3551
 
7.7%
6 3482
 
7.6%
9 3463
 
7.6%
8 3458
 
7.5%
7 3398
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 8987
19.6%
1 4686
10.2%
2 3952
8.6%
3 3861
8.4%
5 3642
7.9%
4 3551
 
7.7%
6 3482
 
7.6%
9 3463
 
7.6%
8 3458
 
7.5%
7 3398
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 8987
19.6%
1 4686
10.2%
2 3952
8.6%
3 3861
8.4%
5 3642
7.9%
4 3551
 
7.7%
6 3482
 
7.6%
9 3463
 
7.6%
8 3458
 
7.5%
7 3398
 
7.4%

YouTube Likes
Text

Missing 

Distinct4283
Distinct (%)> 99.9%
Missing315
Missing (%)6.8%
Memory size36.1 KiB
2025-01-30T16:13:34.998934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.0541424
Min length2

Characters and Unicode

Total characters34512
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4281 ?
Unique (%)99.9%

Sample

1st row1,713,126
2nd row3,486,739
3rd row2,228,730
4th row10,629,796
5th row3,670,188
ValueCountFrequency (%)
159,791 2
 
< 0.1%
3,086,157 2
 
< 0.1%
2,749,668 1
 
< 0.1%
1,825,761 1
 
< 0.1%
437,980 1
 
< 0.1%
69,990 1
 
< 0.1%
14,661,425 1
 
< 0.1%
48,757,673 1
 
< 0.1%
3,080,503 1
 
< 0.1%
4,907,193 1
 
< 0.1%
Other values (4273) 4273
99.7%
2025-01-30T16:13:35.511004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 6630
19.2%
1 3684
10.7%
2 3035
8.8%
3 2971
8.6%
4 2702
7.8%
6 2700
7.8%
5 2642
 
7.7%
7 2618
 
7.6%
9 2570
 
7.4%
8 2497
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 6630
19.2%
1 3684
10.7%
2 3035
8.8%
3 2971
8.6%
4 2702
7.8%
6 2700
7.8%
5 2642
 
7.7%
7 2618
 
7.6%
9 2570
 
7.4%
8 2497
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 6630
19.2%
1 3684
10.7%
2 3035
8.8%
3 2971
8.6%
4 2702
7.8%
6 2700
7.8%
5 2642
 
7.7%
7 2618
 
7.6%
9 2570
 
7.4%
8 2497
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 6630
19.2%
1 3684
10.7%
2 3035
8.8%
3 2971
8.6%
4 2702
7.8%
6 2700
7.8%
5 2642
 
7.7%
7 2618
 
7.6%
9 2570
 
7.4%
8 2497
 
7.2%

TikTok Posts
Text

Missing 

Distinct3318
Distinct (%)96.8%
Missing1173
Missing (%)25.5%
Memory size36.1 KiB
2025-01-30T16:13:35.992424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.7986577
Min length1

Characters and Unicode

Total characters23299
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3242 ?
Unique (%)94.6%

Sample

1st row5,767,700
2nd row674,700
3rd row3,025,400
4th row7,189,811
5th row16,400
ValueCountFrequency (%)
1,100,000 6
 
0.2%
1,800,000 6
 
0.2%
1 4
 
0.1%
9 4
 
0.1%
1,500,000 4
 
0.1%
1,200,000 4
 
0.1%
1,600,000 4
 
0.1%
2,100,000 4
 
0.1%
1,300,000 3
 
0.1%
21,800 3
 
0.1%
Other values (3308) 3385
98.8%
2025-01-30T16:13:36.431292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 3987
17.1%
0 3173
13.6%
1 2557
11.0%
2 2126
9.1%
3 1887
8.1%
4 1797
7.7%
6 1654
7.1%
5 1600
6.9%
7 1550
 
6.7%
8 1505
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 3987
17.1%
0 3173
13.6%
1 2557
11.0%
2 2126
9.1%
3 1887
8.1%
4 1797
7.7%
6 1654
7.1%
5 1600
6.9%
7 1550
 
6.7%
8 1505
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 3987
17.1%
0 3173
13.6%
1 2557
11.0%
2 2126
9.1%
3 1887
8.1%
4 1797
7.7%
6 1654
7.1%
5 1600
6.9%
7 1550
 
6.7%
8 1505
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 3987
17.1%
0 3173
13.6%
1 2557
11.0%
2 2126
9.1%
3 1887
8.1%
4 1797
7.7%
6 1654
7.1%
5 1600
6.9%
7 1550
 
6.7%
8 1505
 
6.5%

TikTok Likes
Text

Missing 

Distinct3615
Distinct (%)99.9%
Missing980
Missing (%)21.3%
Memory size36.1 KiB
2025-01-30T16:13:36.800808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length13
Mean length9.5892265
Min length1

Characters and Unicode

Total characters34713
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3610 ?
Unique (%)99.7%

Sample

1st row651,565,900
2nd row35,223,547
3rd row275,154,237
4th row1,078,757,968
5th row214,943,489
ValueCountFrequency (%)
13,324,305 2
 
0.1%
21,202,350 2
 
0.1%
14,000 2
 
0.1%
1,800,000 2
 
0.1%
42 2
 
0.1%
29,252,686 1
 
< 0.1%
125,499,784 1
 
< 0.1%
6,016,370 1
 
< 0.1%
52,704,504 1
 
< 0.1%
14,675,601 1
 
< 0.1%
Other values (3605) 3605
99.6%
2025-01-30T16:13:37.289355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 6812
19.6%
1 3533
10.2%
2 3158
9.1%
3 2919
8.4%
4 2695
 
7.8%
0 2695
 
7.8%
6 2617
 
7.5%
5 2610
 
7.5%
7 2598
 
7.5%
9 2546
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 6812
19.6%
1 3533
10.2%
2 3158
9.1%
3 2919
8.4%
4 2695
 
7.8%
0 2695
 
7.8%
6 2617
 
7.5%
5 2610
 
7.5%
7 2598
 
7.5%
9 2546
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 6812
19.6%
1 3533
10.2%
2 3158
9.1%
3 2919
8.4%
4 2695
 
7.8%
0 2695
 
7.8%
6 2617
 
7.5%
5 2610
 
7.5%
7 2598
 
7.5%
9 2546
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 6812
19.6%
1 3533
10.2%
2 3158
9.1%
3 2919
8.4%
4 2695
 
7.8%
0 2695
 
7.8%
6 2617
 
7.5%
5 2610
 
7.5%
7 2598
 
7.5%
9 2546
 
7.3%

TikTok Views
Text

Missing 

Distinct3616
Distinct (%)99.9%
Missing981
Missing (%)21.3%
Memory size36.1 KiB
2025-01-30T16:13:37.650060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length10.903012
Min length2

Characters and Unicode

Total characters39458
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3613 ?
Unique (%)99.8%

Sample

1st row5,332,281,936
2nd row208,339,025
3rd row3,369,120,610
4th row14,603,725,994
5th row2,938,686,633
ValueCountFrequency (%)
1,200,000 2
 
0.1%
117,505,652 2
 
0.1%
158,504,854 2
 
0.1%
331,691 1
 
< 0.1%
66,918,543 1
 
< 0.1%
60,797,733 1
 
< 0.1%
61,564,975 1
 
< 0.1%
5,332,281,936 1
 
< 0.1%
267,911,446 1
 
< 0.1%
164,453,741 1
 
< 0.1%
Other values (3606) 3606
99.6%
2025-01-30T16:13:38.147478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 7893
20.0%
1 3913
9.9%
2 3406
8.6%
3 3235
8.2%
0 3149
 
8.0%
4 3097
 
7.8%
5 3038
 
7.7%
8 2974
 
7.5%
9 2926
 
7.4%
7 2924
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39458
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 7893
20.0%
1 3913
9.9%
2 3406
8.6%
3 3235
8.2%
0 3149
 
8.0%
4 3097
 
7.8%
5 3038
 
7.7%
8 2974
 
7.5%
9 2926
 
7.4%
7 2924
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39458
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 7893
20.0%
1 3913
9.9%
2 3406
8.6%
3 3235
8.2%
0 3149
 
8.0%
4 3097
 
7.8%
5 3038
 
7.7%
8 2974
 
7.5%
9 2926
 
7.4%
7 2924
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39458
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 7893
20.0%
1 3913
9.9%
2 3406
8.6%
3 3235
8.2%
0 3149
 
8.0%
4 3097
 
7.8%
5 3038
 
7.7%
8 2974
 
7.5%
9 2926
 
7.4%
7 2924
 
7.4%
Distinct3458
Distinct (%)96.3%
Missing1009
Missing (%)21.9%
Memory size36.1 KiB
2025-01-30T16:13:38.398825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.145642
Min length1

Characters and Unicode

Total characters36433
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3389 ?
Unique (%)94.4%

Sample

1st row150,597,040
2nd row156,380,351
3rd row373,784,955
4th row3,351,188,582
5th row112,763,851
ValueCountFrequency (%)
381,728 13
 
0.4%
2,606,582 7
 
0.2%
19,904,542 7
 
0.2%
45,212,529 6
 
0.2%
934,654 5
 
0.1%
2,119,803 5
 
0.1%
85,756,632 4
 
0.1%
2,297,932 4
 
0.1%
125,856,157 4
 
0.1%
87,239,915 4
 
0.1%
Other values (3448) 3532
98.4%
2025-01-30T16:13:38.721584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 7126
19.6%
1 3764
10.3%
2 3331
9.1%
3 3048
8.4%
5 2979
8.2%
4 2956
8.1%
6 2763
 
7.6%
7 2670
 
7.3%
8 2654
 
7.3%
9 2637
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36433
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 7126
19.6%
1 3764
10.3%
2 3331
9.1%
3 3048
8.4%
5 2979
8.2%
4 2956
8.1%
6 2763
 
7.6%
7 2670
 
7.3%
8 2654
 
7.3%
9 2637
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36433
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 7126
19.6%
1 3764
10.3%
2 3331
9.1%
3 3048
8.4%
5 2979
8.2%
4 2956
8.1%
6 2763
 
7.6%
7 2670
 
7.3%
8 2654
 
7.3%
9 2637
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36433
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 7126
19.6%
1 3764
10.3%
2 3331
9.1%
3 3048
8.4%
5 2979
8.2%
4 2956
8.1%
6 2763
 
7.6%
7 2670
 
7.3%
8 2654
 
7.3%
9 2637
 
7.2%

Apple Music Playlist Count
Real number (ℝ)

High correlation  Missing 

Distinct322
Distinct (%)8.0%
Missing561
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean54.60312
Minimum1
Maximum859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:38.829002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median28
Q370
95-th percentile199
Maximum859
Range858
Interquartile range (IQR)60

Descriptive statistics

Standard deviation71.61227
Coefficient of variation (CV)1.3115051
Kurtosis12.672924
Mean54.60312
Median Absolute Deviation (MAD)22
Skewness2.8881751
Sum220542
Variance5128.3172
MonotonicityNot monotonic
2025-01-30T16:13:38.955591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 150
 
3.3%
2 111
 
2.4%
4 107
 
2.3%
8 105
 
2.3%
3 100
 
2.2%
6 99
 
2.2%
7 92
 
2.0%
14 90
 
2.0%
10 90
 
2.0%
9 83
 
1.8%
Other values (312) 3012
65.5%
(Missing) 561
 
12.2%
ValueCountFrequency (%)
1 150
3.3%
2 111
2.4%
3 100
2.2%
4 107
2.3%
5 82
1.8%
6 99
2.2%
7 92
2.0%
8 105
2.3%
9 83
1.8%
10 90
2.0%
ValueCountFrequency (%)
859 1
< 0.1%
581 1
< 0.1%
554 1
< 0.1%
550 1
< 0.1%
549 1
< 0.1%
513 1
< 0.1%
507 1
< 0.1%
497 1
< 0.1%
470 1
< 0.1%
465 1
< 0.1%

AirPlay Spins
Text

Missing 

Distinct3267
Distinct (%)79.6%
Missing498
Missing (%)10.8%
Memory size36.1 KiB
2025-01-30T16:13:39.307198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.7852267
Min length1

Characters and Unicode

Total characters19629
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2948 ?
Unique (%)71.9%

Sample

1st row40,975
2nd row40,778
3rd row74,333
4th row1,474,799
5th row12,185
ValueCountFrequency (%)
1 69
 
1.7%
2 48
 
1.2%
3 33
 
0.8%
8 23
 
0.6%
5 23
 
0.6%
4 20
 
0.5%
7 16
 
0.4%
6 15
 
0.4%
10 12
 
0.3%
9 11
 
0.3%
Other values (3257) 3832
93.4%
2025-01-30T16:13:39.743340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2845
14.5%
1 2736
13.9%
2 2132
10.9%
3 1796
9.1%
4 1659
8.5%
5 1564
8.0%
6 1482
7.6%
8 1468
7.5%
7 1351
6.9%
9 1302
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2845
14.5%
1 2736
13.9%
2 2132
10.9%
3 1796
9.1%
4 1659
8.5%
5 1564
8.0%
6 1482
7.6%
8 1468
7.5%
7 1351
6.9%
9 1302
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2845
14.5%
1 2736
13.9%
2 2132
10.9%
3 1796
9.1%
4 1659
8.5%
5 1564
8.0%
6 1482
7.6%
8 1468
7.5%
7 1351
6.9%
9 1302
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2845
14.5%
1 2736
13.9%
2 2132
10.9%
3 1796
9.1%
4 1659
8.5%
5 1564
8.0%
6 1482
7.6%
8 1468
7.5%
7 1351
6.9%
9 1302
6.6%

SiriusXM Spins
Text

Missing 

Distinct689
Distinct (%)27.8%
Missing2123
Missing (%)46.2%
Memory size36.1 KiB
2025-01-30T16:13:40.152318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.4376262
Min length1

Characters and Unicode

Total characters6038
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)14.0%

Sample

1st row684
2nd row3
3rd row536
4th row2,182
5th row1
ValueCountFrequency (%)
1 54
 
2.2%
2 52
 
2.1%
4 45
 
1.8%
3 43
 
1.7%
5 36
 
1.5%
7 32
 
1.3%
8 31
 
1.3%
6 29
 
1.2%
9 29
 
1.2%
16 23
 
0.9%
Other values (679) 2103
84.9%
2025-01-30T16:13:40.729879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1162
19.2%
2 801
13.3%
3 679
11.2%
4 593
9.8%
5 557
9.2%
8 464
 
7.7%
6 441
 
7.3%
7 432
 
7.2%
0 403
 
6.7%
9 377
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1162
19.2%
2 801
13.3%
3 679
11.2%
4 593
9.8%
5 557
9.2%
8 464
 
7.7%
6 441
 
7.3%
7 432
 
7.2%
0 403
 
6.7%
9 377
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1162
19.2%
2 801
13.3%
3 679
11.2%
4 593
9.8%
5 557
9.2%
8 464
 
7.7%
6 441
 
7.3%
7 432
 
7.2%
0 403
 
6.7%
9 377
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1162
19.2%
2 801
13.3%
3 679
11.2%
4 593
9.8%
5 557
9.2%
8 464
 
7.7%
6 441
 
7.3%
7 432
 
7.2%
0 403
 
6.7%
9 377
 
6.2%

Deezer Playlist Count
Real number (ℝ)

High correlation  Missing 

Distinct233
Distinct (%)6.3%
Missing921
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean32.310954
Minimum1
Maximum632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:40.831038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median15
Q337
95-th percentile117
Maximum632
Range631
Interquartile range (IQR)32

Descriptive statistics

Standard deviation54.274538
Coefficient of variation (CV)1.6797566
Kurtosis32.518872
Mean32.310954
Median Absolute Deviation (MAD)12
Skewness4.7941641
Sum118872
Variance2945.7255
MonotonicityNot monotonic
2025-01-30T16:13:40.967122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 283
 
6.2%
2 204
 
4.4%
3 198
 
4.3%
4 160
 
3.5%
5 147
 
3.2%
6 132
 
2.9%
7 108
 
2.3%
8 106
 
2.3%
10 86
 
1.9%
13 82
 
1.8%
Other values (223) 2173
47.2%
(Missing) 921
20.0%
ValueCountFrequency (%)
1 283
6.2%
2 204
4.4%
3 198
4.3%
4 160
3.5%
5 147
3.2%
6 132
2.9%
7 108
 
2.3%
8 106
 
2.3%
9 78
 
1.7%
10 86
 
1.9%
ValueCountFrequency (%)
632 1
< 0.1%
584 1
< 0.1%
570 1
< 0.1%
564 1
< 0.1%
557 1
< 0.1%
548 1
< 0.1%
541 1
< 0.1%
512 1
< 0.1%
502 1
< 0.1%
482 1
< 0.1%

Deezer Playlist Reach
Text

Missing 

Distinct3558
Distinct (%)96.9%
Missing928
Missing (%)20.2%
Memory size36.1 KiB
2025-01-30T16:13:41.310516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.9763072
Min length1

Characters and Unicode

Total characters25617
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3488 ?
Unique (%)95.0%

Sample

1st row17,598,718
2nd row10,422,430
3rd row36,321,847
4th row24,684,248
5th row17,660,624
ValueCountFrequency (%)
1,097 17
 
0.5%
1,566,667 7
 
0.2%
72 5
 
0.1%
91,280 5
 
0.1%
71 5
 
0.1%
9,615 5
 
0.1%
9,696 4
 
0.1%
1,370 3
 
0.1%
337,935 3
 
0.1%
18 3
 
0.1%
Other values (3548) 3615
98.4%
2025-01-30T16:13:41.759645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 4428
17.3%
1 2932
11.4%
2 2362
9.2%
3 2198
8.6%
4 2090
8.2%
5 2003
7.8%
9 1944
7.6%
6 1938
7.6%
7 1927
7.5%
8 1924
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 4428
17.3%
1 2932
11.4%
2 2362
9.2%
3 2198
8.6%
4 2090
8.2%
5 2003
7.8%
9 1944
7.6%
6 1938
7.6%
7 1927
7.5%
8 1924
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 4428
17.3%
1 2932
11.4%
2 2362
9.2%
3 2198
8.6%
4 2090
8.2%
5 2003
7.8%
9 1944
7.6%
6 1938
7.6%
7 1927
7.5%
8 1924
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 4428
17.3%
1 2932
11.4%
2 2362
9.2%
3 2198
8.6%
4 2090
8.2%
5 2003
7.8%
9 1944
7.6%
6 1938
7.6%
7 1927
7.5%
8 1924
7.5%

Amazon Playlist Count
Real number (ℝ)

High correlation  Missing 

Distinct147
Distinct (%)4.1%
Missing1055
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean25.348942
Minimum1
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-30T16:13:41.873295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median17
Q334
95-th percentile79
Maximum210
Range209
Interquartile range (IQR)26

Descriptive statistics

Standard deviation25.989826
Coefficient of variation (CV)1.0252825
Kurtosis6.5105035
Mean25.348942
Median Absolute Deviation (MAD)11
Skewness2.1859842
Sum89862
Variance675.47104
MonotonicityNot monotonic
2025-01-30T16:13:41.995140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 142
 
3.1%
8 142
 
3.1%
2 141
 
3.1%
7 128
 
2.8%
3 126
 
2.7%
9 115
 
2.5%
6 108
 
2.3%
5 104
 
2.3%
11 102
 
2.2%
4 102
 
2.2%
Other values (137) 2335
50.8%
(Missing) 1055
22.9%
ValueCountFrequency (%)
1 142
3.1%
2 141
3.1%
3 126
2.7%
4 102
2.2%
5 104
2.3%
6 108
2.3%
7 128
2.8%
8 142
3.1%
9 115
2.5%
10 96
2.1%
ValueCountFrequency (%)
210 1
< 0.1%
189 1
< 0.1%
188 1
< 0.1%
184 1
< 0.1%
177 1
< 0.1%
174 1
< 0.1%
172 1
< 0.1%
168 1
< 0.1%
167 1
< 0.1%
163 1
< 0.1%

Pandora Streams
Text

Missing 

Distinct3491
Distinct (%)99.9%
Missing1106
Missing (%)24.0%
Memory size36.1 KiB
2025-01-30T16:13:42.272406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length9.0821408
Min length1

Characters and Unicode

Total characters31733
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3488 ?
Unique (%)99.8%

Sample

1st row18,004,655
2nd row7,780,028
3rd row5,022,621
4th row190,260,277
5th row4,493,884
ValueCountFrequency (%)
56,972,562 2
 
0.1%
2,829 2
 
0.1%
6,723,858 2
 
0.1%
9,699,636 1
 
< 0.1%
2,129,023 1
 
< 0.1%
21,298,372 1
 
< 0.1%
4,744,421 1
 
< 0.1%
16,658,690 1
 
< 0.1%
404,463,590 1
 
< 0.1%
134,305,701 1
 
< 0.1%
Other values (3481) 3481
99.6%
2025-01-30T16:13:42.920759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 6087
19.2%
1 3267
10.3%
3 2813
8.9%
2 2813
8.9%
4 2571
8.1%
5 2468
7.8%
8 2404
 
7.6%
6 2395
 
7.5%
7 2377
 
7.5%
0 2272
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 6087
19.2%
1 3267
10.3%
3 2813
8.9%
2 2813
8.9%
4 2571
8.1%
5 2468
7.8%
8 2404
 
7.6%
6 2395
 
7.5%
7 2377
 
7.5%
0 2272
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 6087
19.2%
1 3267
10.3%
3 2813
8.9%
2 2813
8.9%
4 2571
8.1%
5 2468
7.8%
8 2404
 
7.6%
6 2395
 
7.5%
7 2377
 
7.5%
0 2272
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 6087
19.2%
1 3267
10.3%
3 2813
8.9%
2 2813
8.9%
4 2571
8.1%
5 2468
7.8%
8 2404
 
7.6%
6 2395
 
7.5%
7 2377
 
7.5%
0 2272
 
7.2%
Distinct2975
Distinct (%)89.3%
Missing1268
Missing (%)27.6%
Memory size36.1 KiB
2025-01-30T16:13:43.366624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.1494598
Min length1

Characters and Unicode

Total characters17158
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2706 ?
Unique (%)81.2%

Sample

1st row22,931
2nd row28,444
3rd row5,639
4th row203,384
5th row7,006
ValueCountFrequency (%)
9 6
 
0.2%
12 5
 
0.2%
2 5
 
0.2%
545 4
 
0.1%
8 4
 
0.1%
14 4
 
0.1%
26 4
 
0.1%
758 4
 
0.1%
100 4
 
0.1%
165 4
 
0.1%
Other values (2965) 3288
98.7%
2025-01-30T16:13:43.839655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2642
15.4%
1 2171
12.7%
2 1740
10.1%
3 1583
9.2%
4 1395
8.1%
5 1380
8.0%
6 1346
7.8%
7 1266
7.4%
8 1239
7.2%
9 1222
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2642
15.4%
1 2171
12.7%
2 1740
10.1%
3 1583
9.2%
4 1395
8.1%
5 1380
8.0%
6 1346
7.8%
7 1266
7.4%
8 1239
7.2%
9 1222
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2642
15.4%
1 2171
12.7%
2 1740
10.1%
3 1583
9.2%
4 1395
8.1%
5 1380
8.0%
6 1346
7.8%
7 1266
7.4%
8 1239
7.2%
9 1222
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2642
15.4%
1 2171
12.7%
2 1740
10.1%
3 1583
9.2%
4 1395
8.1%
5 1380
8.0%
6 1346
7.8%
7 1266
7.4%
8 1239
7.2%
9 1222
7.1%

Soundcloud Streams
Text

Missing 

Distinct1265
Distinct (%)99.8%
Missing3333
Missing (%)72.5%
Memory size36.1 KiB
2025-01-30T16:13:44.135491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.6227309
Min length2

Characters and Unicode

Total characters10925
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1263 ?
Unique (%)99.7%

Sample

1st row4,818,457
2nd row6,623,075
3rd row7,208,651
4th row207,179
5th row9,438,601
ValueCountFrequency (%)
1,336,043 2
 
0.2%
27 2
 
0.2%
1,313,357 1
 
0.1%
12,038,034 1
 
0.1%
871,978 1
 
0.1%
377,734 1
 
0.1%
975,891 1
 
0.1%
1,551,157 1
 
0.1%
36,341,585 1
 
0.1%
1,612,479 1
 
0.1%
Other values (1255) 1255
99.1%
2025-01-30T16:13:44.519930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2159
19.8%
1 1141
10.4%
2 1001
9.2%
3 937
8.6%
5 866
7.9%
4 853
 
7.8%
8 811
 
7.4%
9 802
 
7.3%
7 791
 
7.2%
0 782
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 2159
19.8%
1 1141
10.4%
2 1001
9.2%
3 937
8.6%
5 866
7.9%
4 853
 
7.8%
8 811
 
7.4%
9 802
 
7.3%
7 791
 
7.2%
0 782
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 2159
19.8%
1 1141
10.4%
2 1001
9.2%
3 937
8.6%
5 866
7.9%
4 853
 
7.8%
8 811
 
7.4%
9 802
 
7.3%
7 791
 
7.2%
0 782
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 2159
19.8%
1 1141
10.4%
2 1001
9.2%
3 937
8.6%
5 866
7.9%
4 853
 
7.8%
8 811
 
7.4%
9 802
 
7.3%
7 791
 
7.2%
0 782
 
7.2%

Shazam Counts
Text

Missing 

Distinct4002
Distinct (%)99.5%
Missing577
Missing (%)12.5%
Memory size36.1 KiB
2025-01-30T16:13:44.793980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.7693264
Min length1

Characters and Unicode

Total characters31256
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3987 ?
Unique (%)99.1%

Sample

1st row2,669,262
2nd row1,118,279
3rd row5,285,340
4th row11,822,942
5th row457,017
ValueCountFrequency (%)
1 5
 
0.1%
7 3
 
0.1%
5 3
 
0.1%
3 3
 
0.1%
9 2
 
< 0.1%
24,801 2
 
< 0.1%
16 2
 
< 0.1%
178,962 2
 
< 0.1%
75 2
 
< 0.1%
1,261 2
 
< 0.1%
Other values (3992) 3997
99.4%
2025-01-30T16:13:45.161428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 5825
18.6%
1 3389
10.8%
2 2866
9.2%
3 2661
8.5%
4 2579
8.3%
6 2426
7.8%
5 2368
7.6%
9 2325
 
7.4%
7 2303
 
7.4%
8 2260
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 5825
18.6%
1 3389
10.8%
2 2866
9.2%
3 2661
8.5%
4 2579
8.3%
6 2426
7.8%
5 2368
7.6%
9 2325
 
7.4%
7 2303
 
7.4%
8 2260
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 5825
18.6%
1 3389
10.8%
2 2866
9.2%
3 2661
8.5%
4 2579
8.3%
6 2426
7.8%
5 2368
7.6%
9 2325
 
7.4%
7 2303
 
7.4%
8 2260
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 5825
18.6%
1 3389
10.8%
2 2866
9.2%
3 2661
8.5%
4 2579
8.3%
6 2426
7.8%
5 2368
7.6%
9 2325
 
7.4%
7 2303
 
7.4%
8 2260
 
7.2%

TIDAL Popularity
Unsupported

Missing  Rejected  Unsupported 

Missing4600
Missing (%)100.0%
Memory size36.1 KiB

Explicit Track
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
0
2949 
1
1651 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2949
64.1%
1 1651
35.9%

Length

2025-01-30T16:13:45.246047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T16:13:45.313212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2949
64.1%
1 1651
35.9%

Most occurring characters

ValueCountFrequency (%)
0 2949
64.1%
1 1651
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2949
64.1%
1 1651
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2949
64.1%
1 1651
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2949
64.1%
1 1651
35.9%

Interactions

2025-01-30T16:13:20.908065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:18.066039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:18.757802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:19.544521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:20.258590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:21.043978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:18.188919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:18.923931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:19.704072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:20.405094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:21.191072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:18.291155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:19.058422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:19.859097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:20.535100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:21.356620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:18.414165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:19.192433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:19.998791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:20.648448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:21.492060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:18.560858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:19.367043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:20.135366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T16:13:20.788701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-30T16:13:45.376953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Amazon Playlist CountApple Music Playlist CountDeezer Playlist CountExplicit TrackSpotify PopularityTrack Score
Amazon Playlist Count1.0000.6140.5710.0940.3990.382
Apple Music Playlist Count0.6141.0000.7660.0590.3830.345
Deezer Playlist Count0.5710.7661.0000.1110.3900.321
Explicit Track0.0940.0590.1111.0000.0960.050
Spotify Popularity0.3990.3830.3900.0961.0000.367
Track Score0.3820.3450.3210.0500.3671.000

Missing values

2025-01-30T16:13:21.936021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-30T16:13:22.467774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-30T16:13:23.052702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TrackAlbum NameArtistRelease DateISRCAll Time RankTrack ScoreSpotify StreamsSpotify Playlist CountSpotify Playlist ReachSpotify PopularityYouTube ViewsYouTube LikesTikTok PostsTikTok LikesTikTok ViewsYouTube Playlist ReachApple Music Playlist CountAirPlay SpinsSiriusXM SpinsDeezer Playlist CountDeezer Playlist ReachAmazon Playlist CountPandora StreamsPandora Track StationsSoundcloud StreamsShazam CountsTIDAL PopularityExplicit Track
0MILLION DOLLAR BABYMillion Dollar Baby - SingleTommy Richman4/26/2024QM24S24025281725.4390,470,93630,716196,631,58892.084,274,7541,713,1265,767,700651,565,9005,332,281,936150,597,040210.040,97568462.017,598,718114.018,004,65522,9314,818,4572,669,262NaN0
1Not Like UsNot Like UsKendrick Lamar5/4/2024USUG124009102545.9323,703,88428,113174,597,13792.0116,347,0403,486,739674,70035,223,547208,339,025156,380,351188.040,778367.010,422,430111.07,780,02828,4446,623,0751,118,279NaN1
2i like the way you kiss meI like the way you kiss meArtemas3/19/2024QZJ8424003873538.4601,309,28354,331211,607,66992.0122,599,1162,228,7303,025,400275,154,2373,369,120,610373,784,955190.074,333536136.036,321,847172.05,022,6215,6397,208,6515,285,340NaN0
3FlowersFlowers - SingleMiley Cyrus1/12/2023USSM122097774444.92,031,280,633269,802136,569,07885.01,096,100,89910,629,7967,189,8111,078,757,96814,603,725,9943,351,188,582394.01,474,7992,182264.024,684,248210.0190,260,277203,384NaN11,822,942NaN0
4HoudiniHoudiniEminem5/31/2024USUG124033985423.3107,034,9227,223151,469,87488.077,373,9573,670,18816,400NaNNaN112,763,851182.012,185182.017,660,624105.04,493,8847,006207,179457,017NaN1
5Lovin On MeLovin On MeJack Harlow11/10/2023USAT223113716410.1670,665,438105,892175,421,03483.0131,148,0911,392,5934,202,367214,943,4892,938,686,6332,867,222,632138.0522,0424,65486.017,167,254152.0138,529,36250,9829,438,6014,517,131NaN1
6Beautiful ThingsBeautiful ThingsBenson Boone1/18/2024USWB123070167407.2900,158,75173,118201,585,71486.0308,723,1454,120,760NaN29,584,940534,915,3134,601,579,812280.0383,478429168.048,197,850154.065,447,47657,372NaN9,990,302NaN0
7Gata OnlyGata OnlyFloyyMenor2/2/2024QZL3824060498375.8675,079,15340,094211,236,94092.0228,382,5681,439,4953,500,000338,546,6683,804,584,1632,112,581,620160.017,2213087.033,245,59553.03,372,4285,762NaN6,063,523NaN1
8Danza Kuduro - Coverýýýýýýýýýýýýýýýýýýýýý - ýýýýýýýýýýýýýýýýýý -MUSIC LAB JPN6/9/2024TCJPA24637089355.71,653,018,119115NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1
9BAND4BAND (feat. Lil Baby)BAND4BAND (feat. Lil Baby)Central Cee5/23/2024USSM1240435410330.690,676,57310,400184,199,41986.032,735,244988,682325,800121,574,500974,656,200174,706,874191.03,82311778.010,800,09892.01,005,6268423,679,709666,302NaN1
TrackAlbum NameArtistRelease DateISRCAll Time RankTrack ScoreSpotify StreamsSpotify Playlist CountSpotify Playlist ReachSpotify PopularityYouTube ViewsYouTube LikesTikTok PostsTikTok LikesTikTok ViewsYouTube Playlist ReachApple Music Playlist CountAirPlay SpinsSiriusXM SpinsDeezer Playlist CountDeezer Playlist ReachAmazon Playlist CountPandora StreamsPandora Track StationsSoundcloud StreamsShazam CountsTIDAL PopularityExplicit Track
4590DaylightHarry's HouseHarry Styles5/20/2022USSM122006134,55819.5377,242,48642,23311,501,24068.046,848,6831,141,59380,96139,903,732224,312,08362,344,31221.04,93121822.0581,1628.020,446,0884,196165,194224,414NaN0
4591Happiest YearFeel Something (Deluxe)Jaymes Young6/23/2017USAT219037154,54019.5472,408,27684,22023,944,01869.0181,335,8412,086,2352,033,143440,989,6983,526,073,258140,406,98133.05631310.0101,9201.010,855,29418,606NaN1,724,972NaN0
45923 Haseln�ï¿3 Haseln��sse - SiJaques Raupï¿12/1/2023DEN0623008454,59519.425,822,17615,3872,650,670NaN7,041,124127,9984,0341,858,46631,061,91648,311,5508.0250NaN9.0163,9991.0NaNNaN33,508312,177NaN0
4593Jaragandi (From "Game Changer") (Telugu)Jaragandi (From "Game Changer") (Telugu)Thaman S3/27/2024INH1024055944,58719.43,754,9141011,306,604NaN34,315,313431,343NaNNaNNaNNaN1.0NaNNaNNaNNaN8.0NaNNaNNaN3,169NaN0
4594ýýýýýýýýýýýý (ýýýýýýýýýýýýýý ýýýýýýýýýýýý) [prod. by wex & heysubr]ýýýýýýýýýýýý (ýýýýýýýýýýýýýý ýýýýýýýýýýýý) [prod. by wex & heysubr]BUSHIDO ZHO5/3/2024RUB4224013594,59919.45,092,789685185,93662.010,423,004288,976NaN3,63527,800NaN7.0NaNNaNNaNNaNNaNNaNNaNNaN251,996NaN1
4595For the Last TimeFor the Last Time$uicideboy$9/5/2017QM8DG17034204,58519.4305,049,96365,7705,103,05471.0149,247,7471,397,59048,37020,202,000143,934,37953,0163.06NaN2.014,217NaN20,104,06613,18450,633,006656,337NaN1
4596Dil Meri Na SuneDil Meri Na Sune (From "Genius")Atif Aslam7/27/2018INT1018001224,57519.452,282,3604,6021,449,76756.0943,920,2455,347,766NaN1,72119,93524,973,0481.0412NaN1.0927NaNNaNNaNNaN193,590NaN0
4597Grace (feat. 42 Dugg)My TurnLil Baby2/28/2020USUG120000434,57119.4189,972,68572,0666,704,80265.0201,027,3331,081,4027,5965,288,67736,849,00529,253,15219.0204NaN1.0746.084,426,74028,999NaN1,135,998NaN1
4598Nashe Si Chadh GayiNovember Top 10 SongsArijit Singh11/8/2016INY0916000674,59119.4145,467,02014,0377,387,06466.01,118,595,1593,868,82811,4332,534,83137,757,30125,150,5161.01,200NaNNaNNaN7.06,817,840NaNNaN448,292NaN0
4599Me Acostumbre (feat. Bad Bunny)Me Acostumbre (feat. Bad Bunny)Arc��4/11/2017USB2717001074,59319.4255,740,65332,13814,066,52664.0866,300,7553,826,82978,00010,055,46170,196,388175,831,37611.02,083NaN4.0127,4794.069,006,73911,320NaN767,006NaN1

Duplicate rows

Most frequently occurring

TrackAlbum NameArtistRelease DateISRCAll Time RankTrack ScoreSpotify StreamsSpotify Playlist CountSpotify Playlist ReachSpotify PopularityYouTube ViewsYouTube LikesTikTok PostsTikTok LikesTikTok ViewsYouTube Playlist ReachApple Music Playlist CountAirPlay SpinsSiriusXM SpinsDeezer Playlist CountDeezer Playlist ReachAmazon Playlist CountPandora StreamsPandora Track StationsSoundcloud StreamsShazam CountsExplicit Track# duplicates
0DembowDembowDanny Ocean12/8/2017USWL117002693,44123.3579,189,52660,39711,805,08465.0828,853,6963,086,15784,45021,202,350158,504,854199,705,67934.05,406920.037,64912.06,723,8587,832NaN1,619,55002
1Tennessee OrangeTennessee OrangeMegan Moroney9/2/2022TCAGJ22892542,42428.9227,893,58628,13912,480,71473.030,913,276159,79177,66113,324,305117,505,652238,206,22833.0129,172345.01,37049.056,972,56226,9681,336,043708,14302